Магистратура 2021/2022

Анализ ковариационных моделей

Лучший по критерию «Новизна полученных знаний»

Статус: Курс обязательный (Прикладная статистика с методами сетевого анализа)

Направление: 01.04.02. Прикладная математика и информатика

Кто читает: Международная лаборатория прикладного сетевого анализа

Где читается: Международная лаборатория прикладного сетевого анализа

Когда читается: 2-й курс, 1, 2 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Кускова Валентина Викторовна

Прогр. обучения: Прикладная статистика с методами сетевого анализа

Язык: английский

Кредиты: 6

Контактные часы: 48

Course Syllabus

Abstract

This course is designed for MASNA students who would like to acquire a significant familiarity with the statistical techniques known collectively as "structural equation modeling," "causal modeling," or "analysis of covariance structures."

Learning Objectives

• To provide you with an understanding of the basic principles of latent variable structural equation modeling and lay the foundation for future learning in the area.
• To explore the advantages and disadvantages of latent variable structural equation modeling, and how it relates to other methods of analysis.
• To develop your familiarity, through hands on experience, with the major structural equation modeling programs, so that you can use them and interpret their output.
• To develop and/or foster critical reviewing skills of published empirical research using structural equation modeling.

Expected Learning Outcomes

• Be able to use the major SEM programs to estimate common types of models: Formative indicator models.
• Be able to use the major SEM programs to estimate common types of models: Latent growth curve models, latent state-trait-occasion models, etc.
• Be able to use the major SEM programs to estimate common types of models: Latent variable multi-equation models.
• Be able to use the major SEM programs to estimate common types of models: Models with latent variable interactions.
• Be able to use the major SEM programs to estimate common types of models: Models with multiple mediating effects.
• Be able to use the major SEM programs to estimate common types of models: Multi-equation path analysis models
• Be able to use the major SEM programs to estimate common types of models: Multi-group models with mean structures.
• Be able to use the major SEM programs to estimate common types of models: Multi-level models (If time permits).
• Be able to use the major SEM programs to estimate common types of models: Path models with fixed, non-zero error terms
• Be able to use the major SEM programs to estimate common types of models: Second-order factor models.
• Have a working knowledge of the different ways to analyze models with covariance structures.
• Have an understanding common problems related to model specification, identification, and estimation.
• Know how to translate conceptual thinking into models that can be estimated.
• Know the basic idea of implied matrices and what is happening in SEM.
• Know the major structural equation modeling programs.

Course Contents

• Course Introduction
• Problem Selection and Conceptualization
• Fundamentals of LVSEM (Part 1)
• Basic Model
• Fundamentals of LVSEM (Part 2)
• Fundamentals of LVSEM (Part 3)
• Software Programs
• Observed Variable Models – Path Analysis
• Testing Mediation
• Effect Decomposition
• Measurement Model Specification
• Assessing Construct Validity and Reliability
• Multiple Groups Analysis
• Latent Variable Interactions
• Latent Change Analysis
• Special Topics

Assessment Elements

• Answers to Readings Questions
• Basics Exam
• Path Analysis and Mediating Effects
• Latent Variable Model
• Moderating Effects with Latent Variables
• Special Topic Presentation

Interim Assessment

• 2021/2022 1st module
  0.6 * Basics Exam + 0.4 * Answers to Readings Questions
• 2021/2022 2nd module
  0.25 * Path Analysis and Mediating Effects + 0.25 * Moderating Effects with Latent Variables + 0.25 * Special Topic Presentation + 0.25 * Latent Variable Model

Bibliography

Recommended Core Bibliography

• Netemeyer, R. G., Sharma, S., & Bearden, W. O. (2003). Scaling Procedures : Issues and Applications. Thousand Oaks, Calif: SAGE Publications, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=321358
• Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling (Vol. 2nd ed). Mahwah, NJ: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=188193

Recommended Additional Bibliography

• Byrne, B. M. (1998). Structural Equation Modeling With Lisrel, Prelis, and Simplis : Basic Concepts, Applications, and Programming. Mahwah, N.J.: Psychology Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=582749
• Byrne, B. M. (2000). Structural Equation Modeling With AMOS : Basic Concepts, Applications, and Programming. Mahwah, N.J.: Psychology Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=54805